Modeling a faster revenue cycle
Seismic changes in healthcare payment models are forcing providers to find new ways to increase productivity and revenue while cutting costs.
The challenge is formidable because many hospital payment and billing processes are inefficient and poorly designed.
“Hospitals are experiencing a number of problems,” said Paul Bradley, chief data scientist for ZirMed. “Those include lower reimbursement resulting in margin compression, disparate data systems that result in inconsistent, expensive data and metrics, and data that are updated weekly, monthly or quarterly, which results in a lack of any actionable workflow.”
Among the most effective emerging tools for healthcare providers to streamline and optimize processes while maximizing revenue is predictive analytics. Bradley and Northwestern Memorial Hospital Director Richard Nagengast will lead an educational session at HIMSS15 titled, “Using Data Analytics for Improving Productivity and Revenue.”
Bradley said the session will show how an organization can connect its data with predictive analytics technology using Agile software methodology.
“Every hospital has amassed large data assets that characterize their interactions with patients, physicians, and payers, among others,” he added. “We’ll zero in on the process and work needed so that a hospital can tap into those data assets to operate more efficiently from a revenue cycle perspective.”
Accomplishing this, Bradley said, requires connecting data assets with technology – specifically predictive analytics, data warehousing and reporting.
Implementing predictive analytics can enable a healthcare organization to improve processes and payment collection opportunities, create consistent and measurable metrics across the revenue cycle, and manage staff resources in a way that streamlines workflow and reduces unessential tasks.
“The result should be a system that focuses the right staff on the right accounts at the right time to maximize return and efficiency,” Bradley said. “Ultimately it should allow the revenue cycle staff to work smarter.”
Predictive modeling can be used to estimate the likelihood of a claim denial prior to submission, for example. This allows staff to focus only on “those claims likely to be denied – based on past denial patterns – in order to minimize delays in reimbursement.”
Nagengast will discuss how Northwestern Memorial Hospital’s adoption of predictive analytics has helped reduce manual tasks, increased productivity and maximized revenue.
The session “Using Data Analytics for Improving Productivity and Revenue” will start on Monday, April 13, at 10 AM in Room S100A.